electricsheepasia/asia-food-security-fao-diem-monitoring-system-household-sur

FAO Data in Emergencies Monitoring System (DIEM)

Publisher: Food and Agriculture Organization (FAO) of the United Nations · Source: HDX · License: hdx-other · Updated: 2026-05-05

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Abstract

The Food and Agriculture Organization of the United Nations (FAO) has developed a monitoring system in 26 food crisis countries to better understand the impacts of various shocks on agricultural livelihoods, food security and local value chains.
The Monitoring System consists of primary data collected from households on a periodic basis (more or less every four months, depending on seasonality). Data are collected through Computer-Assisted Telephone Interviews (CATI) and in-person surveys where the circumstances allow for field access.
As the system is developed, the information collected and analyzed is being used to guide strategic decisions, to design programmes and to inform analytical processes such as the Integrated Phase Classification (IPC) and the Humanitarian Needs Overview (HNO).
At the core of the system is a standardized household questionnaire administered to around 150,000 households per year across the 26 countries. Standardization permits comparisons across time and space, considerably enhancing the utility of the data for decision makers. At minimum the household data are representative at Admin 1 level (e.g. province, or region) and in frequent cases at Admin 2 level (e.g. district). In each aggregated field, the values indicate the frequencies of the different responses, expressed as a weighted percentage of the total sample. The present datasets represents aggregated data referring to household interviews performed after December 2022. At every new survey data release, after cleaning and validation phases, aggregated data is appended to the present dataset.

For real-time updates, for accessing archived data or microdata and for additional survey-specific information, please visit the DIEM Hub: https://data-in-emergencies.fao.org/ or contact DIEM

View the column descriptions here. Metadata available here.

Reference administrative boundaries (levels 0, 1 and 2) available here in GIS format.

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the coll_start_date, coll_end_date column(s). Geographic scope: AFG, BGD, BFA, KHM, CMR, CAF, TCD, COL, and 25 others.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Country-level aggregates
Rows (total)	2,511
Columns	230 (217 numeric, 10 categorical, 3 datetime)
Train split	2,008 rows
Test split	502 rows
Geographic scope	AFG, BGD, BFA, KHM, CMR, CAF, TCD, COL, and 25 others
Publisher	Food and Agriculture Organization (FAO) of the United Nations
HDX last updated	2026-05-05

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Variables

Geographic — adm0_iso3 (YEM, BGD, AFG), surveys (range 1.0–2682.0), hh_agricactivity_1 (range 0.0204–99.994), hh_agricactivity_2 (range 0.0573–81.8609), hh_agricactivity_3 (range 0.1326–100.0098) and 9 others.

Temporal — coll_start_date, coll_end_date, coll_mid_date.

Demographic — hh_gender_1 (range 19.0595–100.0124), hh_gender_2 (range 0.0692–80.9522), hh_education_1 (range 0.0364–99.9996), hh_education_2 (range 0.3676–69.8138), hh_education_3 (range 0.067–67.2747) and 49 others.

Outcome / Measurement — income_main_1, income_main_2, income_main_3, income_main_4, income_main_6 and 84 others.

Identifier / Metadata — objectid (range 736.0–7697.0), adm0_name (Yemen, Bangladesh, Afghanistan), adm1_name (Dhaka, Chattogram, Balochistan), adm1_pcode (BD30, BD20, PK22022), adm2_pcode (BD6091, BD6090, BD6058) and 6 others.

Other — adm0_m49 (range 4.0–887.0), adm_level (range 1.0–2.0), round (range 1.0–30.0), tot_crop_producers (range 0.0–2523.0), tot_ls_producers (range 0.0–1542.0) and 54 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-food-security-all")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
objectid	int64	0.0%	736.0 – 7697.0 (mean 4051.5094)
adm0_name	object	0.0%	Yemen, Bangladesh, Afghanistan
adm0_iso3	object	0.0%	YEM, BGD, AFG
adm0_m49	int64	0.0%	4.0 – 887.0 (mean 439.0315)
adm1_name	object	0.0%	Dhaka, Chattogram, Balochistan
adm1_pcode	object	0.0%	BD30, BD20, PK22022
adm2_pcode	object	59.8%	BD6091, BD6090, BD6058
adm2_name	object	59.8%	Sunamganj, Falaba, Maulvibazar
adm_name	object	0.0%	Abyan, Al Maharah, Al Mahwit
adm_pcode	object	0.0%	YE23, YE22, YE15
adm_level	int64	0.0%	1.0 – 2.0 (mean 1.4022)
round	int64	0.0%	1.0 – 30.0 (mean 9.6623)
coll_start_date	datetime64[ns]	0.0%
coll_end_date	datetime64[ns]	0.0%
coll_mid_date	datetime64[ns]	0.0%
surveys	int64	0.0%	1.0 – 2682.0 (mean 187.2736)
tot_crop_producers	float64	18.4%	0.0 – 2523.0 (mean 124.7988)
tot_ls_producers	float64	18.4%	0.0 – 1542.0 (mean 85.5669)
tot_fish_producers	float64	17.5%	0.0 – 286.0 (mean 10.3866)
hh_agricactivity_1	float64	4.7%	0.0204 – 99.994 (mean 27.1802)
hh_agricactivity_2	float64	6.6%	0.0573 – 81.8609 (mean 13.5993)
hh_agricactivity_3	float64	4.1%	0.1326 – 100.0098 (mean 30.8245)
hh_agricactivity_4	float64	11.5%	0.0217 – 97.6869 (mean 31.9715)
hh_gender_1	float64	4.9%	19.0595 – 100.0124 (mean 83.0519)
hh_gender_2	float64	6.5%	0.0692 – 80.9522 (mean 17.2139)
hh_education_1	float64	15.4%	0.0364 – 99.9996 (mean 37.3058)
hh_education_2	float64	15.8%	0.3676 – 69.8138 (mean 26.6322)
hh_education_3	float64	16.8%	0.067 – 67.2747 (mean 18.7961)
hh_education_4	float64	21.4%	0.02 – 64.7391 (mean 12.0769)
hh_education_5	float64	42.5%	0.0287 – 76.8284 (mean 8.8851)
hh_education_888	float64	76.8%	0.0209 – 30.5547 (mean 1.4988)
hh_maritalstat_1	float64	63.8%
hh_maritalstat_2	float64	60.7%
hh_maritalstat_3	float64	76.6%
hh_maritalstat_4	float64	77.5%
hh_maritalstat_5	float64	61.8%
hh_residencetype_1	float64	37.9%
hh_residencetype_2	float64	60.0%
hh_residencetype_3	float64	74.2%
hh_residencetype_4	float64	57.9%
resp_isfishproducer_1	float64	47.7%
resp_iscropproducer_1	float64	18.4%
resp_islsproducer_1	float64	18.5%
income_main_1	float64	18.0%
income_main_2	float64	32.9%
income_main_3	float64	43.9%
income_main_4	float64	20.7%
income_main_6	float64	62.5%
income_main_7	float64	66.9%
income_main_8	float64	35.8%
income_main_9	float64	25.2%
income_main_10	float64	55.3%
income_main_11	float64	25.3%
income_main_12	float64	20.0%
income_main_13	float64	26.4%
income_main_14	float64	25.9%
income_main_15	float64	56.7%
income_main_16	float64	42.3%
income_main_17	float64	45.9%
income_main_18	float64	62.2%
income_main_19	float64	34.0%
income_main_gender_1	float64	39.1%
income_main_gender_2	float64	41.8%
income_main_gender_3	float64	40.9%
income_main_control_1	float64	48.9%
income_main_control_2	float64	50.1%
income_main_control_3	float64	50.3%
income_main_comp_1	float64	36.3%
income_main_comp_2	float64	3.2%
income_main_comp_3	float64	1.1%
income_main_comp_4	float64	1.0%
income_main_comp_5	float64	7.3%
income_main_comp_888	float64	67.7%
income_sec_1	float64	29.3%
income_sec_2	float64	40.6%
income_sec_3	float64	50.7%
income_sec_4	float64	24.5%
income_sec_6	float64	71.6%
income_sec_7	float64	75.7%
income_sec_8	float64	64.0%
income_sec_9	float64	37.5%
income_sec_10	float64	74.2%
income_sec_11	float64	31.7%
income_sec_12	float64	24.1%
income_sec_13	float64	33.9%
income_sec_14	float64	45.1%
income_sec_15	float64	69.7%
income_sec_16	float64	48.9%
income_sec_17	float64	56.2%
income_sec_18	float64	62.4%
income_sec_19	float64	21.1%
income_sec_gender_1	float64	66.1%
income_sec_gender_2	float64	68.7%
income_sec_gender_3	float64	68.3%
income_sec_control_1	float64	79.4%
income_sec_control_2	float64	79.7%
income_sec_comp_1	float64	59.2%
income_sec_comp_2	float64	25.5%
income_sec_comp_3	float64	21.3%
income_sec_comp_4	float64	20.9%
income_sec_comp_5	float64	37.6%
income_sec_comp_888	float64	76.1%
income_third_1	float64	64.3%
income_third_2	float64	71.8%
income_third_3	float64	78.4%
income_third_4	float64	48.3%
income_third_9	float64	75.1%
income_third_11	float64	67.3%
income_third_12	float64	58.1%
income_third_13	float64	70.8%
income_third_16	float64	71.6%
income_third_19	float64	21.1%
income_third_gender_1	float64	79.8%
income_third_comp_2	float64	46.2%
income_third_comp_3	float64	38.4%
income_third_comp_4	float64	44.7%
income_third_comp_5	float64	68.5%
hh_wealth_light_1	float64	60.3%
hh_wealth_light_2	float64	78.5%
hh_wealth_light_3	float64	73.8%
hh_wealth_light_4	float64	61.7%
hh_wealth_light_5	float64	57.4%
hh_wealth_light_666	float64	72.7%
hh_wealth_toilet_1	float64	46.9%
hh_wealth_toilet_2	float64	40.9%
hh_wealth_toilet_3	float64	58.7%
hh_wealth_toilet_4	float64	64.5%
hh_wealth_toilet_777	float64	74.1%
hh_wealth_water_1	float64	55.3%
hh_wealth_water_2	float64	49.1%
hh_wealth_water_3	float64	49.8%
hh_wealth_water_4	float64	70.9%
hh_wealth_water_5	float64	56.8%
hh_wealth_water_6	float64	64.6%
hh_wealth_water_7	float64	55.7%
hh_wealth_water_8	float64	59.2%
hh_wealth_water_9	float64	61.8%
hh_wealth_water_10	float64	74.2%
shock_animaldisease_1	float64	13.7%
shock_coldtemporhail_1	float64	60.6%
shock_drought_1	float64	23.6%
shock_firemanmade_1	float64	78.1%
shock_firenatural_1	float64	79.2%
shock_flood_1	float64	33.0%
shock_higherfoodprices_1	float64	1.3%
shock_higherfuelprices_1	float64	8.4%
shock_hurricane_1	float64	63.5%
shock_landslides_1	float64	77.2%
shock_lostemplorwork_1	float64	4.1%
shock_mvtrestrict_1	float64	55.1%
shock_napasture_1	float64	54.0%
shock_noshock_1	float64	4.0%
shock_othercropandlivests_1	float64	29.0%
shock_othereconomicshock_1	float64	8.5%
shock_otherintrahhshock_1	float64	9.4%
shock_othermanmadehazard_1	float64	49.3%
shock_othernathazard_1	float64	46.2%
shock_pestoutbreak_1	float64	30.3%
shock_plantdisease_1	float64	25.4%
shock_sicknessordeathofhh_1	float64	4.9%
shock_theftofprodassets_1	float64	63.0%
shock_violenceinsecconf_1	float64	41.9%
need_0	float64	25.7%
need_1	float64	18.4%
need_cash_1	float64	18.2%
need_cold_storage_1	float64	73.2%
need_crop_infrastructure_1	float64	22.1%
need_crop_inputs_1	float64	19.9%
need_crop_knowledge_1	float64	26.2%
need_env_infra_rehab_1	float64	50.7%
need_fish_infrastructure_1	float64	56.8%
need_fish_inputs_1	float64	52.3%
need_fish_knowledge_1	float64	60.4%
need_food_1	float64	21.1%
need_ls_feed_1	float64	21.2%
need_ls_infrastructure_1	float64	25.7%
need_ls_knowledge_1	float64	33.3%
need_ls_vet_service_1	float64	23.1%
need_marketing_supp_1	float64	48.1%
need_other_1	float64	50.5%
need_received_cash_1	float64	39.9%
need_received_crop_assist_1	float64	54.6%
need_received_food_1	float64	36.6%
need_received_ls_assist_1	float64	76.6%
need_received_none_1	float64	18.8%
need_received_other_1	float64	71.0%
need_received_vouchers_fair_1	float64	77.4%
need_vouchers_fair_1	float64	38.4%
assistance_quality_1	float64	31.9%
assistance_quality_2	float64	76.9%
assistance_quality_3	float64	68.9%
assistance_quality_4	float64	63.0%
assistance_dk_1	float64	79.7%
assistance_fao_1	float64	78.8%
assistance_gov_1	float64	58.3%
assistance_ngo_1	float64	59.3%
assistance_otherun_1	float64	73.0%
assistance_wfp_1	float64	70.8%
hh_age_median	float64	38.9%
hh_age_wmean	float64	38.9%
hh_age_stddev	float64	0.0%
hh_age_ci_low	float64	38.9%
hh_age_ci_high	float64	38.9%
hh_size_median	float64	23.3%
hh_size_wmean	float64	23.3%
hh_size_stddev	float64	0.0%
hh_size_ci_low	float64	23.3%
hh_size_ci_high	float64	23.3%
tot_income_median	float64	62.0%
tot_income_wmean	float64	62.0%
tot_income_stddev	float64	0.0%
tot_income_ci_low	float64	62.0%
tot_income_ci_high	float64	62.0%
income_main_amount_median	float64	61.8%
income_main_amount_wmean	float64	61.8%
income_main_amount_stddev	float64	0.0%
income_main_amount_ci_low	float64	61.8%
income_main_amount_ci_high	float64	61.8%
income_sec_amount_median	float64	68.1%
income_sec_amount_wmean	float64	68.1%
income_sec_amount_stddev	float64	0.0%
income_sec_amount_ci_low	float64	68.1%
income_sec_amount_ci_high	float64	68.1%
income_third_amount_median	float64	74.0%
income_third_amount_wmean	float64	74.0%
income_third_amount_stddev	float64	0.0%
income_third_amount_ci_low	float64	74.0%
income_third_amount_ci_high	float64	74.0%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-05-05

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Numeric Summary

Column	Min	Max	Mean	Median
objectid	736.0	7697.0	4051.5094	3871.0
adm0_m49	4.0	887.0	439.0315	422.0
adm_level	1.0	2.0	1.4022	1.0
round	1.0	30.0	9.6623	8.0
surveys	1.0	2682.0	187.2736	150.0
tot_crop_producers	0.0	2523.0	124.7988	106.5
tot_ls_producers	0.0	1542.0	85.5669	67.0
tot_fish_producers	0.0	286.0	10.3866	3.0
hh_agricactivity_1	0.0204	99.994	27.1802	18.2415
hh_agricactivity_2	0.0573	81.8609	13.5993	11.433
hh_agricactivity_3	0.1326	100.0098	30.8245	28.9347
hh_agricactivity_4	0.0217	97.6869	31.9715	29.5684
hh_gender_1	19.0595	100.0124	83.0519	87.1382
hh_gender_2	0.0692	80.9522	17.2139	13.0639
hh_education_1	0.0364	99.9996	37.3058	33.4

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 84 column(s) with >80% missing values were removed: hh_agricactivity_888, hh_agricactivity_999, hh_gender_888, hh_gender_999, hh_education_999, hh_maritalstat_888.... 3 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from Food and Agriculture Organization (FAO) of the United Nations and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: adm2_pcode, adm2_name, hh_education_4, hh_education_5, hh_education_888, hh_maritalstat_1, hh_maritalstat_2, hh_maritalstat_3....
• This dataset spans 33 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_asia_food_security_all,
  title     = {FAO Data in Emergencies Monitoring System (DIEM)},
  author    = {Food and Agriculture Organization (FAO) of the United Nations},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/fao-diem-monitoring-system-household-surveys-aggregated-data},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.